A data-driven approach for the preliminary design of ship hull forms has been proposed to address the issues of long design cycles and high manual effort in traditional ship hull form design methods. By focusing on the digital representation of hull lines, database construction, and data storage, classification and retrieval, a method for constructing a hull form database is proposed to enable the visualization of functionalities such as adding, deleting, viewing, modifying, and matching of hull lines. To fully utilize the existing data in the database, a feature extraction function for hull lines is developed. This function segments the ship hull surface and calculates the normal vector, Gaussian curvature and mean curvature of the hull surface, thereby facilitating dimensionality reduction of the hull surface features. A convolutional neural network is then employed with the reduced-dimensional features as inputs to predict the ship resistance in static water. Experimental results show that the database can effectively manage the data of the ship hull form, and the error of the total ship resistance coefficient predicted by the neural network is within 10%. This work enables the inheritance of high-quality data into the preliminary hull form design of new ship types.